Abstract

Finding good image descriptors that can accurately describe the visual aspect of many different classes of images is a challenging task. Such descriptors are easier to compute for specialized databases, where specific prior knowledge can be used to devise a more dedicated description of the image content. On one side, there is rather a subjective problem of the visual content and on the other side there is the very practical need to find a good technical/mathematical description of this same visual content. Since there is no perfect description of visual content (even humans disagree when interpreting images), most methods try to find a good compromise in balancing the different aspects of image content. While image descriptors that concentrate on a single aspect of the visual content (color, shape and texture) are widely employed, we believe that image descriptors which include integrated contributions from several aspects perform better in terms of performance and of the relevance of the returned results to the expectation of the user. In this paper, we introduce the color weighted histograms that intimately integrate color and texture or shape and we validate their quality on multiple ground truth databases. We also introduce a new shape histogram based on the Hough transform that performs better than the classical edge orientation histogram. This is an added value which can improve considerably the quality of the overall results when used in combination with the weighted color histograms. In this paper we present the image descriptors (signatures) we use in our NWCBIR system and we emphasize the important connection that exists between the image descriptors and the quality of the results returned by the CBIR system.

References

No relevant information is available
If you register references through the customer center, the reference information will be registered as soon as possible.